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methylCC: technology-independent estimation of cell type composition using differentially methylated regions
A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if un...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883691/ https://www.ncbi.nlm.nih.gov/pubmed/31783894 http://dx.doi.org/10.1186/s13059-019-1827-8 |
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author | Hicks, Stephanie C. Irizarry, Rafael A. |
author_facet | Hicks, Stephanie C. Irizarry, Rafael A. |
author_sort | Hicks, Stephanie C. |
collection | PubMed |
description | A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC. |
format | Online Article Text |
id | pubmed-6883691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68836912019-12-03 methylCC: technology-independent estimation of cell type composition using differentially methylated regions Hicks, Stephanie C. Irizarry, Rafael A. Genome Biol Method A major challenge in the analysis of DNA methylation (DNAm) data is variability introduced from intra-sample cellular heterogeneity, such as whole blood which is a convolution of DNAm profiles across a unique cell type. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. Current methods to estimate the cell type proportions in whole blood DNAm samples are only appropriate for one technology and lead to technology-specific biases if applied to data generated from other technologies. Here, we propose the technology-independent alternative: methylCC, which is available at https://github.com/stephaniehicks/methylCC. BioMed Central 2019-11-29 /pmc/articles/PMC6883691/ /pubmed/31783894 http://dx.doi.org/10.1186/s13059-019-1827-8 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Hicks, Stephanie C. Irizarry, Rafael A. methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title | methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title_full | methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title_fullStr | methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title_full_unstemmed | methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title_short | methylCC: technology-independent estimation of cell type composition using differentially methylated regions |
title_sort | methylcc: technology-independent estimation of cell type composition using differentially methylated regions |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6883691/ https://www.ncbi.nlm.nih.gov/pubmed/31783894 http://dx.doi.org/10.1186/s13059-019-1827-8 |
work_keys_str_mv | AT hicksstephaniec methylcctechnologyindependentestimationofcelltypecompositionusingdifferentiallymethylatedregions AT irizarryrafaela methylcctechnologyindependentestimationofcelltypecompositionusingdifferentiallymethylatedregions |